SAR-W-MixMAE: SAR Foundation Model Training Using Backscatter Power Weighting
This work addresses the difficulty of using SAR data for foundation models, which is incremental as it adapts existing methods to a specific domain.
The authors tackled the challenge of applying foundation models to Synthetic Aperture Radar (SAR) images by proposing a masked auto-encoder with intensity-based weighting to reduce speckle noise effects, achieving promising results in flood detection tasks compared to a baseline.
Foundation model approaches such as masked auto-encoders (MAE) or its variations are now being successfully applied to satellite imagery. Most of the ongoing technical validation of foundation models have been applied to optical images like RGB or multi-spectral images. Due to difficulty in semantic labeling to create datasets and higher noise content with respect to optical images, Synthetic Aperture Radar (SAR) data has not been explored a lot in the field for foundation models. Therefore, in this work as a pre-training approach, we explored masked auto-encoder, specifically MixMAE on Sentinel-1 SAR images and its impact on SAR image classification tasks. Moreover, we proposed to use the physical characteristic of SAR data for applying weighting parameter on the auto-encoder training loss (MSE) to reduce the effect of speckle noise and very high values on the SAR images. Proposed SAR intensity-based weighting of the reconstruction loss demonstrates promising results both on SAR pre-training and downstream tasks specifically on flood detection compared with the baseline model.